When people think of data engineers, the description usually stops at “building high-quality pipelines that deliver analyst-ready data.” That is true, but incomplete. In modern organizations, data engineers hold a deeper responsibility. They are not just the builders of pipelines—they are the curators of the business logic itself.
The manufacturing industry learned decades ago that catching defects early in the production process saves exponentially more money than fixing them after products ship. Today’s data engineering teams face a strikingly similar challenge.
Because the real differentiator in today’s market isn’t just having data. It’s about having it simplified, integrated, trusted, and continually improving. It’s having a data team you can trust. And control.
When launching a groundbreaking pharmaceutical product, the stakes and the rewards couldn’t be higher. This blog dives into the remarkable journey of a data team that achieved unparalleled efficiency using DataOps principles and software that transformed their analytics and data teams into a hyper-efficient powerhouse.
What do you do when you have thousands of data pipelines in production? Is there a way that you can visualize what is happening in production quickly and easily?
DataOps Quality Circles are focused teams within data and analytics organizations that meet weekly or monthly to drive continuous improvement, quality automation, and operational efficiency. By leveraging the principles of DataOps, these circles ensure that data processes are error free, consistent, and aligned with business goals.
Dive into DataOps and Data Observabiity with DataKitchen’s expansive free training and certification offerings tailored for individual Data Analytics, Science, and Engineering contributors. From grasping the foundational principles through the free DataOps Cookbook, over 30,000 readers strong, to hands-on certification courses in DataOps, Data Observability, and Automation, each pathway illuminates critical skills and insights. Moreover, senior managers can elevate their teams with advanced DataOps Change Management strategies, making every step from theory to certification educational and transformational.
Christopher Bergh detailed the company’s release of new open-source tools to enhance DataOps practices by addressing common inefficiencies and errors within data teams. During the webinar, he demonstrated how these tools provide robust data observability and automated testing to improve productivity and reliability across data operations.
Why open source DataOps Observability and DataOps TestGen? Our decision to share full-featured versions of these products stems from DataKitchen’s long-standing commitment to enhancing productivity for data teams and promoting the use of automated, observed, and trusted tools. It aligns with our company’s philosophy of sharing knowledge and now software to inspire teams to implement DataOps effectively.
Gil Benghiat, co-founder of Data Kitchen, began by explaining the overarching goal of achieving data team excellence, which involves delivering business value quickly and with high quality. He detailed data teams’ everyday challenges, such as balancing speed and quality, and the impact of Agile methodologies borrowed from software development practices.
DataOps Data Quality TestGen:
Simple, Fast, Generative Data Quality Testing, Execution, and Scoring.
[Open Source, Enterprise]
DataOps Observability:
Monitor every data pipeline, from source to customer value, & find problems fast
[Open Source, Enterprise]
DataOps Automation:
Orchestrate and automate your data toolchain with few errors and a high rate of change.
[Enterprise]
DataOps Consulting, Coaching, and Transformation
Commercial Data & Analytics Platform for Pharma
Data Production Teams
Data Science/AI
Data Engineering
Data Quality
Business Analytics
Data Products
Data Mesh
Data Contracts
ModelOps / MLOps
DataGovOps
Self-Service Operations
Data Quality Assessments
Data Quality Testing
Data Observability
Data Orchestration
Monitor every Data Journey in an enterprise, from source to customer value, in development and production.
Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.
Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.
